Disclosed herein are systems and methods for joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank1. In some embodiments, a method comprises determining a signal of interest (SOI) that is shared by a plurality of type-specific signatures for a plurality of data types; and determining a sparse linear model of the shared SOI based on non-zero entries of a plurality of sparse eigenarrays.

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

A method for analyzing biological reaction systems is provided. The method includes receiving an image of a substrate including a plurality of reaction sites after a biological reaction has taken place. Next, the method includes removing a noise background from the first image. The method includes determining an initial position of each reaction site based on an intensity threshold to generate a initial position set, then refining the initial position set of each reaction site based on an expected pattern of locations of the plurality of reaction sites to generate a first refined position set. The method further includes determining a presence or absence of a fluorescent emission from each reaction site based on the first refined position set and the first image.

A method for quantifying labels on a substrate is performed by an electronic device with one or more processors and memory. The method includes obtaining digital data corresponding to a multi-dimensional measurement over the substrate; identifying a first set of sub-portions of the digital data; and, for a respective sub-portion of the first set of sub-portions of the digital data: increasing a quantity of labels, and subtracting a reference signal distribution from the respective sub-portion to obtain subtracted sub-portion data. The method also includes obtaining subtracted digital data. The subtracted digital data includes the subtracted sub-portion data for the respective sub-portion. The method further includes identifying a second set of one or more sub-portions of the subtracted digital data; and, for a respective sub-portion of the second set of one or more sub-portions of the subtracted digital data, increasing a quantity of labels.

A particle detection method detects presence and location of particles on a target using measured signals from a plurality of structured illumination patterns. The particle detection method uses measured signals obtained by illuminating the target with structured illumination patterns to detect particles. Specifically, the degree of variation in these measured signals in raw images is calculated to determine whether a particle is present on the target at a particular area of interest.

A device comprising a printer configured to apply a code of printed content on a substrate of a product based on a printer technology type, the code having a plurality of digits. The device includes an optical code detector, executed by one or more processors, to detect the code in a received image of the product printed by the printer by optically recognizing characters in the received image using a trained optical character recognition (OCR) algorithm for the printer technology type. The OCR algorithm is trained to identify each digit of the plurality of digits of the code in a region of interest (ROI) based on at least one product parameter to which the printed content is directly applied and the printer technology type. A system and method are also provided.

A method for variant calling in a next generation sequencing analysis pipeline involves obtaining a plurality of sequence reads that each include a nucleotide aligned at a nucleotide position within a sample genome. The method also involves obtaining a plurality of alleles associated with the nucleotide position. The method further involves determining that a particular allele of the plurality of alleles matches one or more sequence reads of the plurality of sequence reads, wherein the particular allele is located at the nucleotide position. Additionally, the method involves generating an image based on information associated with the plurality of sequence reads. Further, the method involves determining, by providing the generated image to a trained neural network, a likelihood that the sample genome contains the particular allele. The method may also involves providing an output signal indicative of the determined likelihood.

Various embodiments of the disclosure relate to systems and methods for aligning a sequence read to a graph reference. In one embodiment, the method comprises selecting a first node from a graph reference, the graph reference comprising a plurality of nodes connected by a plurality of directed edges, at least one node of the plurality of nodes having a nucleotide sequence. The method further comprises traversing the graph reference according to a depth-first search, and comparing a sequence read to nucleotide sequences generated from the traversal of the graph reference. The traversal of the graph is then modified in response to a determination that each and every node associated with a given nucleotide sequence was previously evaluated.

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

Apparatus and methods are described including successively acquiring a plurality of microscopic images of a portion of a blood sample, and tracking motion of pixels within the successively acquired microscopic images. Trypomastigote parasite candidates within the blood sample are identified, by identifying pixel motion that is typical of trypomastigote parasites. It is determined that the blood sample is infected with trypomastigote parasites, at least partially in response thereto. An output is generated indicating that that the blood sample is infected with trypomastigote parasites. Other applications are also described.